Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus.

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis.

(ICKEDS 2004). GECAD--Knowledge Engineering and ICINCO Secretariat Decision Support Research Group Escola Superior de Tecnologia de Setubal Rua Dr. Antonio Bernardino Almeida / Campus do IPS

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.

We consider the two-fold problem of representing collective beliefs and aggregating these beliefs. We propose a novel representation for collective beliefs that uses modular, transitive relations over possible worlds. They allow us to represent conflicting opinions and they have a clear semantics, thus improving upon the quasi-transitive relations often used in social choice. We then describe a way to construct the belief state of an agent informed by a set of sources of varying degrees of reliability. This construction circumvents Arrow's Impossibility Theorem in a satisfactory manner by accounting for the explicitly encoded conflicts. We give a simple set-theory-based operator for combining the information of multiple agents. We show that this operator satisfies the desirable invariants of idempotence, commutativity, and associativity, and, thus, is well-behaved when iterated, and we describe a computationally effective way of computing the resulting belief state. Finally, we extend our framework to incorporate voting.

All accepted papers will appear in the conference proceedings published by AAAI Press. Selected authors will be invited to submit extended versions of their Ingrid Russell, University of Hartford papers to a special issue of the International Journal on Artificial Intelligence Tools irussell@hartford.edu The papers Valerie Barr, Hofstra University should not exceed 5 pages and is due by October 24, 2003. All submissions will be done Zdravko Markov, Central Connecticut State electronically via FLAIRS web submission system, which will be available through University the conference website. Please consult the conference web page for details on paper submission.

Chair: Terry Payne (trp@ecs.soton.ac.uk) nators should contact candidates prior Tentative Organizing AI Alert newsletter, which highlights they be elected. The deadline for Committee: Lloyd Greenwald selected features from the "AI in the nominations is November 1, 2003. Please mark your calendars now for Stanford University. Be sure Symposia/symposia.html) and will be and the Sixteenth Innovative Applications to visit the AI Topics web site at mailed to all AAAI members. Submissions of Artificial Intelligence Conference www.aaai.org/AITopics/aitopics.html will be due to the organizers on (IAAI-04)!

Simulations are an excellent tool for studying AI. However, the simulation technology in use by, and designed for, the AI community often fails to take advantage of much of the work in the larger simulation community to produce stable, repeatable, and efficient simulations. I present SPADES (SYSTEM FOR PARALLEL-AGENT DISCRETE-EVENT SIMULATION) as a simulation substrate for the AI community. SPADES focuses on the agent as a fundamental simulation component. The "thinking time" of an agent is tracked and reflected in the results of the agents' actions. SPADES supports and manages the distribution of agents across machines while it is robust to variations in network performance and machine load. SPADES is not tied to any particular simulation and is a powerful new tool for creating simulations for the study of AI.

I'm delighted to bring our readers the news of an exciting resource for AAAI members. AAAI has now completed a major initiative, begun five years ago, to develop a digital library of AAAI publications. The collection now comprises approximately 13,000 papers, including the full set of papers from the AAAI proceedings, papers from other major conferences, AAAI workshop and symposium technical reports, selected AAAI Press books, and the full contents of AI Magazine. This already-extensive collection is a growing resource, with new publications and access methods to be added over time. I encourage readers to visit it at the members' library section of the AAAI web site, www.aaai.org.